Part II - California Housing Price¶

by Timothy Adebisi¶

Investigation Overview¶

The overall goal of this presentation is to show how housing prices varies according to location and proximity to the ocean. The main features are housing_median_age (Years), median_income (USD), median_house_value (USD) and ocean_proximity.

Dataset Overview¶

The data analyzed is the California housing price dataset downloaded from kaggle. The dataset contains 20,640 observations and 10 features. The features are listed below:

  1. longitude
  2. latitude
  3. housing_median_age (Years)
  4. total_rooms
  5. total_bedrooms
  6. population
  7. households
  8. median_income (USD)
  9. median_house_value (USD)
  10. ocean_proximity

Income and House Value¶

There is a positive correlation between households income and house value as shown in the plot below:

Housing Age and House Value¶

The age of the House does not have any impact on the value placed on the house.

Housing Location, Price and House Value¶

The location of the houses have impact on the value of the house. The closer they are to the Waters, the higher the value

Generate Slideshow: Once you're ready to generate your slideshow, use the jupyter nbconvert command to generate the HTML slide show. . From the terminal or command line, use the following expression.

This should open a tab in your web browser where you can scroll through your presentation. Sub-slides can be accessed by pressing 'down' when viewing its parent slide. Make sure you remove all of the quote-formatted guide notes like this one before you finish your presentation! At last, you can stop the Kernel.